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洞察 - Multimodal Language Models for Amharic
Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages
Amharic LLaMA and LLaVA aim to enhance language models for low resource languages like Amharic through data augmentation and multimodal capabilities.
Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages
The author explores training a multimodal language model, LLaMA-2, to understand Amharic by augmenting data through translation and incorporating visual instruction tuning. This work aims to address the challenges faced by low-resource languages in leveraging large language models effectively.
